Tacit Knowledge Management with Generative AI: Proposal of the GenAI SECI Model
This paper proposes the GenAI SECI model, an update to Nonaka & Takeuchi's classic SECI framework for knowledge creation, designed to leverage generative AI for managing workplace ("Gen-Ba") tacit knowledge. The central innovation is "Digital Fragmented Knowledge"—partial, fragmentary knowledge stored in cyberspace that generative AI aggregates, structures, and recommends to amplify human understanding without requiring full externalization into explicit knowledge. This addresses the urgent problem of transferring expert tacit knowledge as Japan's workforce ages, going beyond conventional KM systems that struggle with the effort required to formalize knowledge.
The GenAI SECI model offers a conceptually coherent framework that positions generative AI as an auxiliary tool (not a new actor) for facilitating human-led "symbol grounding" through workshops. The distinction between treating AI as an assistant versus an autonomous knowledge creator (as in competing GRAI and AKI models) represents a thoughtful theoretical stance with practical implications for human-centric knowledge management. However, the paper operates almost entirely at the conceptual level—no empirical evaluation, no technical implementation details, and concerning citation practices involving papers dated 2026.
The distinction between explicit knowledge, latent knowledge, and tacit knowledge (narrow sense) as continuous layers is well-grounded in Collins' (2010) taxonomy and usefully adapted to workplace contexts. The concept of "Digital Fragmented Knowledge"—explicit and latent knowledge accumulated as partial "knowledge fragments" in cyberspace—captures something important about how generative AI can work with incomplete, non-systematized information that traditional KM systems failed to handle. The positioning of generative AI for externalization (aggregating fragments), combination (structuring via knowledge graphs), and internalization (recommending for workshops) represents a complete and logically consistent workflow.
The paper cites Böhm & Durst (2026) and Kirchner & Scarso (2026) as "very recent papers published in 2026," which appears problematic if the current date precedes 2026—these sources may not exist or are improperly dated. The PDF metadata shows creation date D:20260323204231+09'00' (March 23, 2026), suggesting this paper itself may be pre-dated or from a future submission. The distinction between "Digital Fragmented Knowledge" and Kirchner & Scarso's "Artificial Knowledge" hinges on whether knowledge is presented as "organized, explicit form" versus "knowledge fragments"—yet both involve AI-generated content in cyberspace, and this distinction may be overstated. The claim that "much of Collective Tacit Knowledge can in fact be partially codified" is asserted without evidence. The "Digital Knowledge Twin System" is described as "currently under development" with "full integration and evaluation of generative AI remain tasks for the future"—making this a proposal paper without working implementation or empirical validation.
The comparison with GRAI and AKI models (Table 1) correctly identifies that both treat generative AI as a "new actor" whereas GenAI SECI treats AI as an "auxiliary tool." However, without access to the cited 2026 papers, readers cannot verify whether these characterizations are accurate. The claim that GRAI "extends [the SECI model] to eight interactions" by adding human-machine dimensions is asserted but not demonstrated. The comparison would be stronger with systematic analysis rather than selective quotation. The paper cites workshop studies (Ogawa et al., 2024, 2025) but these appear to be preliminary evaluations of specific components, not the complete system.
Reproducibility is severely limited. No code, datasets, hyperparameters, or implementation details are provided for the Digital Knowledge Twin System. The system is described at a high architectural level only. Key technical questions—what LLM is used, how "semi-automatic aggregation" actually works, what the knowledge graph schema looks like, how recommendations are generated—remain unanswered. The paper notes the Smart Voice Messaging System has been in development since 2010, but no link to code or data is provided. Given this is a conceptual paper for a system "under development," independent reproduction is currently impossible. Future work must include technical specifications, open-source code, and evaluation datasets to advance beyond a theoretical proposal.
The emergence of generative AI is bringing about a significant transformation in knowledge management. Generative AI has the potential to address the limitations of conventional knowledge management systems, and it is increasingly being deployed in real-world settings with promising results. Related research is also expanding rapidly. However, much of this work focuses on research and practice related to the management of explicit knowledge. While fragmentary efforts have been made regarding the management of tacit knowledge using generative AI, the modeling and systematization that handle both tacit and explicit knowledge in an integrated manner remain insufficient. In this paper, we propose the "GenAI SECI" model as an updated version of the knowledge creation process (SECI) model, redesigned to leverage the capabilities of generative AI. A defining feature of the "GenAI SECI" model is the introduction of "Digital Fragmented Knowledge", a new concept that integrates explicit and tacit knowledge within cyberspace. Furthermore, a concrete system architecture for the proposed model is presented, along with a comparison with prior research models that share a similar problem awareness and objectives.
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